English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88866/118573 (75%)
Visitors : 23567106      Online Users : 187
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/116567


    Title: Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data
    Authors: 吳柏林
    Lin, Pei-Chun
    Wu, Berlin
    Watada, Junzo
    Contributors: 應數系
    Keywords: Weight Function; Fuzzy Numbe; Appendix Table ;Triangular Fuzzy Number ;Empirical Distribution Function 
    Date: 2010
    Issue Date: 2018-03-27 15:57:10 (UTC+8)
    Abstract: The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function. When we deal with fuzzy data, it is essential to know how to find the empirical distribution function for continuous fuzzy data. In our paper, we define a new function, the weight function that can be used to deal with continuous fuzzy data. Moreover we can divide samples into different classes. The cumulative function can be calculated with those divided data. The paper explains that the K-S two sample test for continuous fuzzy data can make it possible to judge whether two independent samples of continuous fuzzy data come from the same population. The results show that it is realistic and reasonable in social science research to use the K-S two-sample test for continuous fuzzy data.
    Relation: Advances in Soft Computing, Springer Verlag, pp.175-186
    Integrated Uncertainty Management and Applications pp 175-186
    Data Type: book/chapter
    DOI 連結: https://doi.org/10.1007/978-3-642-11960-6_17
    DOI: 10.1007/978-3-642-11960-6_17
    Appears in Collections:[應用數學系] 專書/專書篇章

    Files in This Item:

    File Description SizeFormat
    10.1007_978-3-642-11960-6_17.pdf214KbAdobe PDF136View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback